Leverage an end-to-end system that features the full set of capabilities needed to improve your model’s performance.
Quickly search, explore and manage your data in one place. Accelerate AI development with raw data, metadata, and ground truth labels at your fingertips.Learn more
Access a full suite of labeling, collaboration, and quality tools that give you complete visibility and control over data labeling operations with in-house labeling teams and labeling service vendors. Leverage automation and custom workflows to make progress as quickly as possible.Learn more
Improve your model with better data. Model is the command center for data-centric iterations, including model error analysis, mining for edge cases, finding and fixing label quality issues, and more.Learn more
Access world-class data labeling services on-demand. Get started immediately with a labeling workforce designed for your needs. Quickly scale up or down as your AI initiatives evolve. Use Labelbox Boost for everything from the smallest to the largest labeling projects.Learn more
Technology and software
How Deque uses data prioritization and model diagnostics to unlock AI breakthroughs in digital accessibility
It takes an average of about 10 minutes to fully annotate a single webpage screen for accessibility compliance. Between the thousands of web datasets and mobile datasets, the Deque team amassed a trove of useful data and needed to automate their manual efforts.
The Deque team leveraged Model Diagnostics and Catalog to target their model’s weaknesses and detect noise issues in their datasets.
Deque was able to rapidly filter out 1/3 of data points considered less trustworthy to improve model performance by 5%+. Annually, Deque is now able to reduce their labeling spend and needs by over 50%.
How a Fortune 500 creative software company improved the speed of their AI development by 50%
Technology and software
How Burberry harnesses Labelbox and Databricks to curate their strategic marketing assets
Retail and ecommerce
Improve model performance through fast and impactful data-centric iterations.
Find the data that will boost model performance using active learning and model error analysis. Save time and money by focusing resources where your specific model needs the most help.Learn more
Achieve up to 80% in labeling efficiency gains with model-assisted labeling – use models to pre-label data, and let humans focus on corrective actions to generate ground truth so they don’t need to start from scratch.Learn more